Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Blood extracellular vesicles carrying brain-specific mRNAs are potential biomarkers for detecting gene expression changes in the female brain

Abstract

The absence of non-invasive tests that can monitor the status of the brain is a major obstacle for psychiatric care. In order to address this need, we assessed the feasibility of using tissue-specific gene expression to determine the origin of extracellular vesicle (EV) mRNAs in peripheral blood. Using the placenta as a model, we discovered that 26 messenger RNAs that are specifically expressed in the placenta are present in EVs circulating in maternal blood. Twenty-three of these transcripts were either exclusively or highly expressed in maternal blood during pregnancy only and not in the postpartum period, verifying the feasibility of using tissue-specific gene expression to infer the tissue of origin for EV mRNAs. Using the same bioinformatic approach, which provides better specificity than isolating L1 cell-adhesion molecule containing EVs, we discovered that 181 mRNAs that are specifically expressed in the female brain are also present in EVs circulating in maternal blood. Gene set enrichment analysis revealed that these transcripts, which are involved in synaptic functions and myelination, are enriched for genes implicated in mood disorders, schizophrenia, and substance use disorders. The EV mRNA levels of 13 of these female brain-specific transcripts are associated with postpartum depression (adjusted p-vals = 3 × 10−5 to 0.08), raising the possibility that they can be used to infer the state of the brain. In order to determine the extent to which EV mRNAs reflect transcription in the brain, we compared mRNAs isolated from cells and EVs in an iPSC-derived brain microphysiological system differentiated for 3 and 9 weeks. We discovered that, although cellular and extracellular mRNA levels are not identical, they do correlate, and it is possible to extrapolate cellular RNA expression changes in the brain via EV mRNA levels. Our findings bring EV mRNAs to the forefront of peripheral biomarker development efforts in psychiatric diseases by demonstrating the feasibility of inferring transcriptional changes in the brain via blood EV mRNA levels.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Summary of experiments.
Fig. 2: Placenta specific gene mRNAs in blood are present at high levels during the pregnancy but not the postpartum period.
Fig. 3: Proteins used for neural derived EV isolation lack brain specificity.
Fig. 4: Brain specific EV mRNAs in blood associate with postpartum depression.
Fig. 5: Brain organoid characterization.
Fig. 6: Cellular and EV mRNA levels are correlated in bMPS organoids.

Similar content being viewed by others

Code availability

The R code used for the analysis in this manuscript is available upon request.

Data availability

The sequencing data is available from the NCBI Sequence Read Archive under Bioproject PRJNA1006349.

References

  1. Fiandaca MS, Kapogiannis D, Mapstone M, Boxer A, Eitan E, Schwartz JB, et al. Identification of preclinical Alzheimer’s disease by a profile of pathogenic proteins in neurally derived blood exosomes: a case-control study. Alzheimers Dement. 2015;11:600.e601.

    Article  PubMed  Google Scholar 

  2. Jia L, Qiu Q, Zhang H, Chu L, Du Y, Zhang J, et al. Concordance between the assessment of Aβ42, T-tau, and P-T181-tau in peripheral blood neuronal-derived exosomes and cerebrospinal fluid. Alzheimers Dement. 2019;15:1071–80.

    Article  PubMed  Google Scholar 

  3. Norman M, Ter-Ovanesyan D, Trieu W, Lazarovits R, Kowal EJK, Lee JH, et al. L1CAM is not associated with extracellular vesicles in human cerebrospinal fluid or plasma. Nat Methods. 2021;18:631–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Osborne LM, Payne JL, Sherer ML, Sabunciyan S. Altered extracellular mRNA communication in postpartum depression is associated with decreased autophagy. Mol Psychiatry. 2022;27:4526–35.

    Article  CAS  PubMed  Google Scholar 

  5. Vorperian SK, Moufarrej MN, Tabula Sapiens C, Quake SR. Cell types of origin of the cell-free transcriptome. Nat Biotechnol. 2022;40:855–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Osborne LM, Voegtline K, Standeven LR, Sundel B, Pangtey M, Hantsoo L, et al. High worry in pregnancy predicts postpartum depression. J Affect Disord. 2021;294:701–6.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Pontén F, Schwenk JM, Asplund A, Edqvist PH. The Human Protein Atlas as a proteomic resource for biomarker discovery. J Intern Med. 2011;270:428–46.

    Article  PubMed  Google Scholar 

  8. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580–5.

    Article  Google Scholar 

  9. Tran MN, Maynard KR, Spangler A, Huuki LA, Montgomery KD, Sadashivaiah V, et al. Single-nucleus transcriptome analysis reveals cell-type-specific molecular signatures across reward circuitry in the human brain. Neuron. 2021;109:3088–3103.e5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Subhash S, Kanduri C. GeneSCF: a real-time based functional enrichment tool with support for multiple organisms. BMC Bioinforma. 2016;17:365.

    Article  Google Scholar 

  11. Bullen CK, Hogberg HT, Bahadirli-Talbott A, Bishai WR, Hartung T, Keuthan C, et al. Infectability of human BrainSphere neurons suggests neurotropism of SARS-CoV-2. Altex. 2020;37:665–71.

    PubMed  Google Scholar 

  12. Smirnova L, Harris G, Delp J, Valadares M, Pamies D, Hogberg HT, et al. A LUHMES 3D dopaminergic neuronal model for neurotoxicity testing allowing long-term exposure and cellular resilience analysis. Arch Toxicol. 2016;90:2725–43.

    Article  CAS  PubMed  Google Scholar 

  13. Harris G, Hogberg H, Hartung T, Smirnova L. 3D differentiation of LUHMES cell line to study recovery and delayed neurotoxic effects. Curr Protoc Toxicol. 2017;73:11.23.11–11.23.28.

    Article  Google Scholar 

  14. Roehr JT, Dieterich C, Reinert K. Flexbar 3.0 - SIMD and multicore parallelization. Bioinformatics. 2017;33:2941–2.

    Article  CAS  PubMed  Google Scholar 

  15. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12:357–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Darby MM, Leek JT, Langmead B, Yolken RH, Sabunciyan S. Widespread splicing of repetitive element loci into coding regions of gene transcripts. Hum Mol Genet. 2016;25:4962–82.

    CAS  PubMed  Google Scholar 

  17. Hicks SC, Okrah K, Paulson JN, Quackenbush J, Irizarry RA, Bravo HC. Smooth quantile normalization. Biostatistics. 2018;19:185–98.

    Article  PubMed  Google Scholar 

  18. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Pamies D, Barreras P, Block K, Makri G, Kumar A, Wiersma D, et al. A human brain microphysiological system derived from induced pluripotent stem cells to study neurological diseases and toxicity. Altex. 2017;34:362–76.

    Article  PubMed  Google Scholar 

  21. Huang Q, Tang B, Romero JC, Yang Y, Elsayed SK, Pahapale G, et al. Shell microelectrode arrays (MEAs) for brain organoids. Sci Adv. 2022;8:eabq5031.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Romero JC, Berlinicke C, Chow S, Duan Y, Wang Y, Chamling X, et al. Oligodendrogenesis and myelination tracing in a CRISPR/Cas9-engineered brain microphysiological system. Front Cell Neurosci. 2022;16:1094291.

    Article  CAS  PubMed  Google Scholar 

  23. Leite PEC, Pereira MR, Harris G, Pamies D, Dos Santos LMG, Granjeiro JM, et al. Suitability of 3D human brain spheroid models to distinguish toxic effects of gold and poly-lactic acid nanoparticles to assess biocompatibility for brain drug delivery. Part Fibre Toxicol. 2019;16:22.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Zhong X, Harris G, Smirnova L, Zufferey V, Sá R, Baldino Russo F, et al. Antidepressant paroxetine exerts developmental neurotoxicity in an iPSC-derived 3D human brain model. Front Cell Neurosci. 2020;14:25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Modafferi S, Zhong X, Kleensang A, Murata Y, Fagiani F, Pamies D, et al. Gene environment interactions in developmental neurotoxicity - a case study of synergy between chlorpyrifos and CHD8 knockout in human BrainSpheres. EHP. 2021;129:77001.

    Article  CAS  PubMed  Google Scholar 

  26. Pulliam L, Sun B, Mustapic M, Chawla S, Kapogiannis D. Plasma neuronal exosomes serve as biomarkers of cognitive impairment in HIV infection and Alzheimer’s disease. J Neurovirol. 2019;25:702–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Shi M, Kovac A, Korff A, Cook TJ, Ginghina C, Bullock KM, et al. CNS tau efflux via exosomes is likely increased in Parkinson’s disease but not in Alzheimer’s disease. Alzheimers Dement. 2016;12:1125–31.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Jones RC, Karkanias J, Krasnow MA, Pisco AO, Quake SR, Salzman J, et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science. 2022;376:eabl4896.

    Article  CAS  PubMed  Google Scholar 

  29. Hasselmann DO, Rappl G, Tilgen W, Reinhold U. Extracellular tyrosinase mRNA within apoptotic bodies is protected from degradation in human serum. Clin Chem. 2001;47:1488–9.

    Article  CAS  PubMed  Google Scholar 

  30. Gupta AK, Holzgreve W, Huppertz B, Malek A, Schneider H, Hahn S. Detection of fetal DNA and RNA in placenta-derived syncytiotrophoblast microparticles generated in vitro. Clin Chem. 2004;50:2187–90.

    Article  CAS  PubMed  Google Scholar 

  31. Drag MH, Kilpeläinen TO. Cell-free DNA and RNA-measurement and applications in clinical diagnostics with focus on metabolic disorders. Physiol Genomics. 2021;53:33–46.

    Article  CAS  PubMed  Google Scholar 

  32. Gilazieva Z, Ponomarev A, Rutland C, Rizvanov A, Solovyeva V. Promising applications of tumor spheroids and organoids for personalized medicine. Cancers. 2020;12:2727.

  33. Modabbernia A, Velthorst E, Reichenberg A. Environmental risk factors for autism: an evidence-based review of systematic reviews and meta-analyses. Mol Autism. 2017;8:13.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Maleki M, Noorimotlagh Z, Mirzaee SA, Jaafarzadeh N, Martinez SS, Rahim F, et al. An updated systematic review on the maternal exposure to environmental pesticides and involved mechanisms of autism spectrum disorder (ASD) progression risk in children. Rev Environ Health. 2022;38:727–740.

  35. Lin C-K, Chang Y-T, Lee F-S, Chen S-T, Christiani D. Association between exposure to ambient particulate matters and risks of autism spectrum disorder in children: a systematic review and exposure-response meta-analysis. Environ Res Lett. 2021;16:063003.

  36. Kessler RC, McGonagle KA, Swartz M, Blazer DG, Nelson CB. Sex and depression in the National Comorbidity Survey. I: lifetime prevalence, chronicity and recurrence. J Affect Disord. 1993;29:85–96.

    Article  CAS  PubMed  Google Scholar 

  37. Wapeesittipan P, Joshi A. Integrated analysis of robust sex-biased gene signatures in human brain. Biol Sex Differ. 2023;14:36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rollins B, Martin MV, Morgan L, Vawter MP. Analysis of whole genome biomarker expression in blood and brain. Am J Med Genet B Neuropsychiatr Genet. 2010;153B:919–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Tylee DS, Kawaguchi DM, Glatt SJ. On the outside, looking in: a review and evaluation of the comparability of blood and brain “-omes”. Am J Med Genet B Neuropsychiatr Genet. 2013;162B:595–603.

    Article  PubMed  Google Scholar 

  40. Hess JL, Quinn TP, Zhang C, Hearn GC, Chen S, Neuropsychiatric Consortium for A. et al. BrainGENIE: the brain gene expression and network imputation engine. Transl Psychiatry. 2023;13:98.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Nishitani S, Isozaki M, Yao A, Higashino Y, Yamauchi T, Kidoguchi M, et al. Cross-tissue correlations of genome-wide DNA methylation in Japanese live human brain and blood, saliva, and buccal epithelial tissues. Transl Psychiatry. 2023;13:72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Braun PR, Han S, Hing B, Nagahama Y, Gaul LN, Heinzman JT, et al. Genome-wide DNA methylation comparison between live human brain and peripheral tissues within individuals. Transl Psychiatry. 2019;9:47.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Hannon E, Lunnon K, Schalkwyk L, Mill J. Interindividual methylomic variation across blood, cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes. Epigenetics. 2015;10:1024–32.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Edgar RD, Jones MJ, Meaney MJ, Turecki G, Kobor MS. BECon: a tool for interpreting DNA methylation findings from blood in the context of brain. Transl Psychiatry. 2017;7:e1187.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sommerer Y, Ohlei O, Dobricic V, Oakley DH, Wesse T, Sedghpour Sabet S, et al. A correlation map of genome-wide DNA methylation patterns between paired human brain and buccal samples. Clin Epigenetics. 2022;14:139.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Ms. Ou Chen for her technical help. This study was funded by the Stanley Medical Research Institute and the following NIH grants NIH-NIMH R01 MH112704, NIH-NIMH 1K23 MH110607 R01ES034554. The authors acknowledge the Integrated Imaging Center and the Advanced Research Computing at Hopkins (ARCH) core facility at Johns Hopkins University. This publication was partially developed under Assistance Agreement No RD83950501 awarded by the U.S. EPA to LS. It had not been formally reviewed by EPA. The views expressed in the publication are solely those of LS and co-authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication.

Author information

Authors and Affiliations

Authors

Contributions

LS, LMO, JLP and SS conceived the idea and designed the initial experiments. LS, SM, CS performed the bMPS experiments. LMO and JLP collected the postpartum depression samples, characterized the patients and selected the cohorts used in the study. SS performed the EV assays, constructed NGS libraries and performed bioinformatic analysis. Everyone was involved in data interpretation and writing of the paper.

Corresponding author

Correspondence to Sarven Sabunciyan.

Ethics declarations

Competing interests

JLP has served as a consultant for SAGE Therapeutics, Brii Biosciences, and Pure Tech Health. JLP has received an honorarium from Karuna Therapeutics for speaking to the company. JLP owns a patent entitled “Epigenetic Biomarkers of Postpartum Depression.” The other authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Smirnova, L., Modafferi, S., Schlett, C. et al. Blood extracellular vesicles carrying brain-specific mRNAs are potential biomarkers for detecting gene expression changes in the female brain. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-023-02384-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41380-023-02384-6

Search

Quick links